Abstract
Safety enhancement is the priority of every organization. Annually millions of dollars are spent to develop procedures and practices to diminish the health, safety and environmental disasters. Monitoring incidents and hazards and performing timely analysis leads to effective remediation efforts. In oil and gas upstream industry, a thorough understanding of HSE data and intelligent data analytics is essential to improve safety and particularly to reduce injuries and fatalities.
Most of the conventional HSE management and hazard identification systems are incapable of agile and automated data integration and smart decision making. Furthermore, HSE incident database is often too intricate to comprehend and its analysis is solely dependent on personal skills of individuals. In order to extract explanatory features' relationship and intelligently make execution strategies, a big data analytics platform is developed. This code enhances the value and quality of information entered into the HSE management systems, consequently prevents occupational hazards and results in a safer workplace. This smart platform quantifies the associated risk with every HSE decision. Oil and gas industry incidents based on area, data type and case were queried from the big data database.
This algorithm was executed on a large public industry data base of occupational injury and illnesses to enhance big data HSE management. This code explored 3 million records categorized based on several parameters including super sector (major industry), industry (sub sector), data type, case type (data sub type), area (i.e. states), year. This paper exemplifies the value of advanced big data analytics to improve safety leadership. Capturing and analyzing such extremely large datasets is impossible with traditional methods.
